266 results about "Decision strategy" patented technology

The invention relates to the technical field of information retrieval, and discloses a multi-modal fusion and reinforcement learning collaborative retrieval enhancement generation method and system. The method comprises the following steps: receiving an original query input by a user, and generating a sub-query based on a large language model in combination with a multi-modal context of a current iteration step; forming a current state in combination with the sub-query and the multi-modal context, modeling a retrieval enhancement generation task as a Markov decision process, and adaptively selecting an optimal action from a predefined action set in the current state by utilizing a large language model according to a decision strategy; executing a corresponding multi-modal retrieval operation according to the optimal action, fusing the obtained multi-modal information, generating an intermediate answer or a final answer of the sub-query, and updating a multi-modal context by using the intermediate answer; off-line training optimization is carried out on the large language model through imitation learning and a calibration chain, and decision strategies and sub-queries are inferred online through the model after fine adjustment. According to the invention, more efficient and accurate complex query processing is realized.

The invention relates to the technical field of AI interaction data processing, and discloses an AI interaction data processing system based on multi-modal perception and dynamic decision, comprising the following modules: a multi-modal perception module used for collecting environment data through a multi-source sensor; the data fusion module is used for generating fused feature data; the causal decision-making module is used for generating a decision-making action to cope with the change of the environment; the decision security module is used for identifying potential safety hazards in the high-risk scene and generating alternative decision or early warning information; the sensing calibration module is used for optimizing a sensing strategy in a changing environment; and the adaptive optimization module continuously optimizes the perception and decision strategy. According to the invention, the multi-modal sensing module is combined with a cross-modal consistency learning mechanism and a noise robustness enhancement technology, and the data fusion module introduces a context sensing attention mechanism and a multi-level feature alignment network, so that the sensing ability of the system to complex environment information and the comprehensiveness and accuracy of feature representation are effectively improved.

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The invention provides a vehicle lane changing planning method and system based on a graph neural network and multiple agents, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining a traffic scene graph structure with multiple agents; using a multi-agent reinforcement learning algorithm and a corresponding reward function to train the graph neural network fusion model, and updating parameters of the graph neural network fusion model by minimizing dominant function-based strategy gradient loss and value function loss to obtain a trained graph neural network fusion model; analyzing the traffic scene graph structure by using the trained graph neural network fusion model to obtain a multi-agent decision strategy; and performing decision-making prior fusion on the multi-agent decision-making strategy, performing dynamic interaction with the environment, obtaining a vehicle lane changing planning result, and completing the vehicle lane changing planning. According to the method, the problems of low multi-agent trajectory tracking precision and poor robustness in a complex scene are solved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention discloses a deep learning-based cold resistance evaluation system for medicago sativa L., and the system comprises a data generation module which is used for generating a phenotypic image and corresponding physiological data of medicago sativa L. under low-temperature stress through a diffusion model embedded with plant low-temperature response physical constraints; the evaluation model module is used for extracting cold resistance characteristics from the image and physiological data by adopting a causal-driven dual-channel adaptive network; and the decision module comprises a hierarchical model distillation unit and a federal reinforcement learning unit, and the hierarchical model distillation unit and the federal reinforcement learning unit realize joint training of model compression and decision strategy optimization through an edge-cloud collaborative architecture, output a cold-resistant decision and realize visualization through an augmented reality interface. The method can effectively solve the core problems of traditional medicago sativa cold resistance assessment in the aspects of data generation, model generalization, decision-making efficiency and the like.

The invention provides an intelligent e-commerce behavior event decision-making method and system fusing multi-source perception, and the method comprises the steps: obtaining a user behavior data set, a commodity attribute data set and an environment context data set through a preset data collection interface, and carrying out the intention recognition processing of multi-source data; generating an intention feature set containing preliminary intention features and refined intention features, then generating an interactive guidance strategy tree containing node branch weights and strategy execution priorities based on the intention feature set, and performing dynamic path adjustment processing on the interactive guidance strategy tree according to a real-time feedback data set to obtain an optimized strategy tree set; and finally, pushing the optimization strategy tree set to a target interaction interface to activate an interaction guide operation, thereby providing personalized and intelligent interaction guide for the user by fusing multi-source sensing data, deeply understanding the intention of the user and dynamically adjusting a decision strategy, and improving the operation efficiency of an e-commerce platform and the satisfaction of the user.

The invention discloses an execution file processing method and system of a decision engine, and relates to the technical field of information. Creating a global execution context according to the parameter set; establishing a parameter hierarchical pool structure according to the global execution context; loading the entity object data to the parameter hierarchical pool structure according to the access authority configured by the association rule; obtaining an association rule corresponding to the decision set number from a preset rule base according to the decision set number; sorting the association rules according to the priorities and historical execution success rates of the association rules; sequentially executing the sorted association rules through a rule execution engine by utilizing a parameter hierarchical pool structure to obtain an association rule execution result set; selecting a predefined decision strategy; and taking the association rule execution result set as input, and calculating a decision result according to the selected decision strategy. Aiming at low rule execution efficiency in the prior art, the execution efficiency is improved.

The invention relates to a multi-competency intelligent scoring method and system, and belongs to the technical field of intelligent evaluation and talent evaluation. The method comprises the following steps: acquiring multi-source response data of a target evaluation object, executing evidence sufficiency and consistency analysis for each competency dimension, generating a judgment state identifier, and constructing a structural description; performing responsibility distribution on the evaluation evidence, mapping the evaluation evidence into a corresponding evidence model and generating a priority constraint relationship; performing time sequence consistency and stability analysis on the evidence model, and constructing a time sequence constraint rule to prohibit unconstrained score backtracking; and carrying out resolution processing on the continuous undetermined dimension, generating a scoring result through a preset risk scoring rule and a conservative determination strategy, and synchronously generating a scoring basis path and confidence information. According to the method, standard management and control of multi-source evidences and accurate constraint of the whole scoring process are realized, the scoring accuracy and traceability are effectively improved, and reliable support is provided for multi-scene talent evaluation decision.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention relates to the field of artificial intelligence cloud computing, in particular to a cloud native application intention driven intelligent arrangement system based on a large language model. The intention analysis module is used for receiving input data and carrying out feature extraction and vectorization decomposition; outputting an intention vector representation; the strategy planning module is used for receiving the intention vector representation, generating a decision strategy through a multi-objective optimization algorithm and outputting an arrangement strategy; the arrangement execution module is used for receiving the arrangement strategy, analyzing the arrangement strategy through a feature mapping network, converting the arrangement strategy into an instruction set and outputting execution result data; the model training module is used for receiving execution result data and input data and generating a weight updating event when parameters change; and the autonomous learning module is used for receiving the weight updating event, carrying out neural network processing, generating an improved arrangement strategy and forming an adaptive learning mechanism. According to the method, the natural language intention is analyzed through the large language model, and the accuracy and robustness of large-scale arrangement are improved in combination with the data-driven decision.

The embodiment of the invention provides a data classification and grading processing method and device, a medium and a program product, and relates to the technical field of data classification. The method comprises the steps of collecting multi-source data corresponding to tasks to be classified and graded, and extracting multi-modal features of the multi-source data by using a pre-trained multi-modal feature extraction model; processing the multi-modal features by using a pre-trained classification and grading model to obtain a data classification and grading result output by the classification and grading model; wherein the classification and grading model is formed by fusing a pre-trained multi-modal large model and a pre-trained vertical domain small model; and determining a final classification and grading decision strategy based on the classification and grading result. According to the embodiment of the invention, the data classification and grading task in the specific field is carried out in a mode of combining the large model and the small model, and the universality and the field specificity of data processing are effectively considered, so that the data classification and grading accuracy can be improved while the data privacy is protected.

The invention discloses a burying point analysis system for customer behavior analysis, and relates to the field of customer behavior analysis, the system comprises the following components: a data acquisition module, a digital twin model construction module, a business scene simulation module and a behavior prediction and decision support module; according to the invention, the digital twinborn model which accurately reflects customer behavior characteristics is constructed by using the digital twinborn model construction module and combining a deep learning algorithm and a data mining technology, and the model is imported into a diversified virtual business scene for behavior simulation through the business scene simulation module, so that the customer behavior simulation efficiency is improved. And finally, the behavior prediction and decision support module accurately predicts customer behaviors and generates a personalized decision plan by applying a space-time correlation prediction algorithm and an intelligent decision recommendation algorithm based on a knowledge graph, so that the customer behaviors can be accurately predicted. The functions jointly enhance the insight ability of enterprises for customer behaviors.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention relates to the technical field of energy systems, and discloses an energy system intelligent decision-making method, which comprises the following steps: acquiring four-flow data of an industrial energy system, and constructing a four-flow parameter model based on a production flow topological graph; constructing a multi-agent reinforcement learning environment, taking the four-flow parameter model as a state space of the multi-agent reinforcement learning environment, and determining an action space and a reward function of each agent; performing domain adjustment on the large language model based on the industrial energy system knowledge base; through combination of a multi-agent reinforcement learning model and a large language model, a decision strategy is dynamically adjusted according to real-time data feedback, and decision feasibility verification is performed based on a physical mechanism model. According to the method, the four-flow integrated parameter model is constructed, and a multi-agent reinforcement learning and large language model cooperation mechanism is introduced, so that the problems of data splitting and the like in a traditional energy management system are effectively solved, and the intelligent level of energy system decision making is improved.

The invention discloses a laser operation process control system based on multi-element fusion, and particularly relates to the field of automatic control, which comprises a multi-element perception fusion module, an intelligent decision module, a multi-actuator cooperative control module, a system general control and man-machine interaction module and a self-learning and optimization module, geometric morphology, a temperature field, plasma features and environmental data of an operation area are collected in real time through integration of multiple sensors, a fusion situation information graph is generated through feature extraction and fusion, forward prediction is carried out in combination with a high-fidelity digital twin model, morphology evolution, thermodynamic behaviors and potential defects of a machining area are predicted, and the machining precision is improved. Based on the prediction result, a laser, a movement mechanism and an auxiliary gas unit are synchronously controlled through a high-speed industrial network, collaborative operation, coordination task scheduling, state monitoring and man-machine interaction of multiple actuators are achieved, and model parameters and decision strategies are continuously optimized by comparing actual data with the prediction result.

The invention provides a multi-agent collaborative decision-making system and method for intelligent manufacturing, and relates to the technical field of multi-agent collaboration. Multi-source heterogeneous sensing data are collected and processed, and various sensing feature data are extracted; constructing an edge computing node, deploying a convolutional neural network model at the edge computing node, processing various sensing feature data, and judging abnormal conditions of various sensing data; constructing a digital twinborn model, and when an abnormal condition of an edge computing node is received, integrating various sensing feature data by the digital twinborn model, generating a collaborative decision strategy, and performing collaborative control on multiple groups of agents; and task allocation is performed on a plurality of intelligent monomers in each group of intelligent agents based on an operation cost function, a capability balance condition and a priority, so that real-time perception and dynamic optimization of the manufacturing process are realized.

The invention relates to the technical field of building electromechanical equipment, and discloses an energy efficiency management system for low-carbon and energy-saving operation of building electromechanical equipment, which comprises a random dynamic modeling module, a self-adaptive economic weight module and an energy efficiency management module, the self-adaptive economic weight module is connected with the random dynamic modeling module and the self-adaptive economic weight module and used for calling the probabilistic prediction model according to external macroscopic state information and the attenuation state inside the equipment, and the random prediction control module is connected with the random dynamic modeling module and the self-adaptive economic weight module and used for calling the probabilistic prediction model. And the data processing module is connected with the random dynamic modeling module and the self-adaptive economic weight module and is used for processing the actual operation data of the equipment. According to the method, a double-circulation online learning module is adopted, dynamic adjustment of a prediction model and a decision strategy is achieved through a high-frequency feedback mechanism and a low-frequency feedback mechanism, and the adaptive capacity of the system to environment changes is greatly improved through the method.

The invention relates to the technical field of unmanned scheduling, in particular to a task scheduling method and system based on an unmanned forklift cloud platform. The method comprises the following steps: obtaining a vehicle state flow and an operation instruction flow of an unmanned forklift group, and constructing a vehicle state task set and an operation instruction task set according to a timestamp; generating a scheduling decision feature set based on the vehicle state task set and the operation instruction task set; performing priority mapping on the scheduling decision feature set to generate a first decision instruction set; performing resource adaptation on the scheduling decision feature set to generate a second decision strategy set; therefore, by constructing a data-driven scheduling mechanism based on time sequence alignment, feature extraction, dual-channel decision and closed-loop correction, the problems of data isolation, single feature, decision lag and lack of feedback optimization in the traditional unmanned forklift scheduling process are solved; and the accuracy of task allocation, the stability of system operation and the execution efficiency of the whole operation are improved.

The invention discloses a vector evaluation gradient multi-objective optimization algorithm for data center cooling system decision making. The overall thought comprises the steps that multi-source sensor data related to a cooling system is obtained according to data center machine room operation logic; performing corresponding preprocessing on data acquired by the sensor to obtain an overall sample; designing a vector evaluation gradient multi-objective optimization algorithm oriented to a data center cooling system decision; the optimization algorithm comprises the definition of a multi-objective optimization problem, uniformly distributed initial populations, population division based on vector evaluation and gradient updating. The optimal decision of the air supply amount and the air supply temperature of the cooling equipment in the cooling system is mainly obtained through an optimization algorithm, and optimal decision parameters are sent to the cooling equipment so as to achieve optimal control over the cooling equipment of the data center. The algorithm provided by the invention ensures that the decision strategy of the cooling equipment can be reasonably and quickly given, so that the appropriate temperature of the machine room and the energy conservation of the cooling equipment are simultaneously realized to meet the requirement of efficient operation of the data center. The energy consumption of the data center can be reduced, and the method is of great significance in promoting green and sustainable development of the data center.

The invention provides a computer network data information identification system, which relates to the technical field of computer network security and data processing and comprises an edge cloud collaborative data acquisition module, a multi-modal feature fusion module, a federated learning dynamic model training module and an intelligent decision and response module. The method has the advantages that the edge cloud collaborative architecture is adopted, data preprocessing and feature extraction are conducted on the network edge, the transmission quantity and delay are reduced, and the real-time performance and the processing efficiency are improved; a self-attention mechanism is used for fusing multi-modal features, so that the recognition accuracy is improved; the data privacy security is protected based on a federated learning framework training model; a reinforcement learning algorithm is introduced to optimize federated learning and decision strategies, and the adaptability and intelligence of the system are enhanced; and response abnormal data can be intelligently dispatched according to an identification result to ensure safe and stable operation of the network.

The invention relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a multi-agent communication and decision-making method, device, equipment and medium. The method comprises the following steps: obtaining state information and communication demand information of each agent in a multi-agent system, determining a communication weight based on the information, a dynamic communication topology is constructed in the system, an intelligent agent is used as a graph node, information of neighbor nodes in communication connection with the node is aggregated to generate interactive feature information, a corresponding decision strategy is generated by combining state information of the intelligent agent and the interactive feature information, and joint modeling and optimization of communication and decision are achieved. According to the method, dynamic topology construction is driven through communication weight, interactive feature information is introduced in strategy generation, and a collaborative optimization mechanism is formed by communication and decision, so that redundant communication is reduced, the communication efficiency is improved, and the multi-agent collaborative precision and the task execution effect are enhanced.

The invention relates to the technical field of computer risk control, in particular to a big-data-based collaborative self-optimization method and system for a self-growth risk control system, and the method comprises the steps: obtaining the operation data of the risk control system, so as to construct a state vector; inputting the state vector into a reinforcement learning agent, and selecting one coordination action from a preset coordination action space for output; the coordination action is executed in the risk control system, and a scalar reward value used for evaluating the effect of the coordination action is obtained through calculation; and forming an empirical data tuple by using the state vector, the coordination action, the scalar reward value and a new state of the system after the action is executed, and training the reinforcement learning agent to update a decision strategy of the reinforcement learning agent. According to the risk control system, the rule engine and the model engine in the risk control system can be considered as a unified whole, and respective parameters are dynamically and cooperatively adjusted, so that the overall comprehensive efficiency of the system is maximized, and the self-adaptive capability and the iteration efficiency of the system are improved.

The invention discloses an injection molding production optimization method based on multi-agent cooperation, and the method comprises the following steps: decomposing a plurality of targets in an injection molding production process, and constructing a layered multi-agent structure; collecting data in the injection molding production process in real time, and constructing a global expert behavior track and a local expert behavior track; based on the global target and each local target, obtaining initial parameters of a global reward function and a local reward function; adopting an inverse reinforcement learning method to obtain an optimal global reward function and an optimal local reward function; obtaining an optimal decision strategy of each agent through a hierarchical collaboration mechanism by each hierarchical agent; when detecting that a conflict exists between the targets, dynamically adjusting the reward weight of the reward function by adopting a conflict coordination mechanism; and continuously collecting new expert behavior data, and updating the optimal decision strategy of each agent. According to the invention, a layered multi-agent inverse reinforcement learning method is adopted, and multi-target dynamic collaborative optimization of injection molding production is realized.

The invention provides a space-air-ground integrated ecological monitoring method and system based on multi-source data fusion, and the method comprises the steps: collecting a multi-source heterogeneous data set, carrying out the space-time alignment, multi-scale fusion and dynamic deduction, and generating an ecological change dynamic deduction result; and carrying out ecological monitoring early warning and decision support based on the result. According to the method, cross-platform data is safely cleaned and enhanced through a federated learning framework, and data modal differences are eliminated; deep fusion static feature regression and time sequence trend prediction are carried out by using a double-model architecture, and microenvironment detail changes and long-term ecological trends are synchronously captured; and finally, through a decision strategy model driven by a double-target reward function, quantifying an ecological restoration cost-benefit ratio and a stability gain as an optimal path, forming a'data fusion-dynamic deduction-decision support 'full-link closed loop, improving ecological monitoring space-time continuity, trend prediction accuracy and management decision scientificity, and improving ecological monitoring efficiency. The contradiction that in the prior art, a user can see widely but cannot see finely, and the user can measure finely but does not tend to measure is overcome.

The invention relates to the technical field of pilotless automobiles, in particular to a local obstacle avoidance decision-making method and system based on a dynamic risk map and a real-time game, and the method comprises the steps: obtaining the state information of an automobile and the sensing data of the surrounding environment through a multi-mode sensor; predicting a future trajectory based on the real-time state information of the dynamic obstacle; constructing a rasterized dynamic risk map which takes the vehicle as the center and is updated in real time, and distributing a comprehensive risk value for each grid; the interaction modeling of the self-vehicle and the dynamic obstacle is a non-cooperative game model, the dynamic risk map and the final prediction trajectory of the dynamic obstacle are used as input, the Nash equilibrium of the non-cooperative game model is solved in real time, and the optimal decision strategy of the self-vehicle is output; and converting the optimal decision strategy into a travelable trajectory conforming to vehicle dynamics constraints, and outputting the travelable trajectory to a vehicle control module to execute local obstacle avoidance. The method overcomes the defects of fixed parameters and poor adaptability of a traditional method, and gives consideration to the safety and high efficiency in different scenes.

The invention relates to the technical field of power systems, and discloses a power system node attribution and reasoning method, system and device and a medium, and the method comprises the steps: obtaining first device data of a target power system; performing first preprocessing on the first equipment data, and establishing a time-varying causal graph model according to a first preprocessing result; and solving the time-varying causal graph model to obtain a decision strategy. According to the method, the accuracy and efficiency of power system safety monitoring are improved, and faults can be quickly positioned and repaired. Through feature extraction of preprocessing, key information is extracted to support data requirements of a time-varying causal graph model. The introduced time-varying causal graph model considers the time factor and the causal relationship, so that the operation state of the system is reflected more accurately. A decision strategy obtained by solving the model ensures safe operation of the power system, and power failure and economic loss caused by faults are reduced.

The invention provides a catering distribution information intelligent management system based on big data, and the system comprises the steps: reading real-time distribution data collected by distribution equipment, and inputting the collected original information flow into a data processing module; executing a three-level serial cleaning operation on the collected original data to generate a structured data set, and inputting the structured data set into an anomaly detection engine; detecting the cleaned data, and outputting a detection result; performing classification processing according to detection results, and inputting arbitration results and manual intervention records into a self-optimization knowledge base; dynamically updating the anomaly detection rule, and feeding back an optimized decision strategy to a related module; and executing a path optimization algorithm and a resource scheduling strategy on the normal data, generating a distribution instruction and issuing the distribution instruction to an execution terminal. The method effectively solves the problems that a traditional system is low in data processing efficiency and lags behind abnormal response, and has the remarkable advantages of improving distribution timeliness and service quality.